Modeling and Optimization
Code
1307
Academic unit
null
Department
null
Credits
7,5
Teacher in charge
Paulo Bárcia
Teaching language
English
Objectives
Optimization techniques are based on quantitative methods, and the general goal is to find the optimal way of designing and operating a system, usually under conditions of scarcity.
This course is an introduction to linear optimization and its extensions, and will be taught by example, solving real world problems from the main functional areas of business (finance, operations, resource economics and marketing) with computer software, optimization formulations and algorithms. Modeling problems from these areas will require students to think about what they are trying to achieve, what the constraints are, what the decision variables are, and how the decision variables relate to both constraints and problem objectives. Student?s ability to structure complex problems and to derive solutions that can improve their insights and ability to make good management decisions are the main skills to be developed during the course. The course will start by emphasizing model formulation and model building as well as the interpretation of software outputs. Special emphasis will be given to the Solver tool in Excel.
The course will start by emphasizing model formulation and model building as well as the interpretation of software outputs. Special emphasis will be given to the Solver tool in Excel®.
Prerequisites
Mandatory Precedence:
- 1303. Linear Algebra
Subject matter
1. Introducing Operations Research
2. Linear Programming
3. Duality and Sensitivity Analysis
4. Transportation Problems
5. Integer Programming
6. Network Models
Bibliography
Winston W. (2004). Operations Research, Applications and Algorithms 4th ed; International student edition. South-Western, Cengage Learning. ISBN: 978-0-534- 42362-9.
Luenberger D. and Ye Y. (2008). Linear and Nonlinear Programming. Springer ISBN: 978- 3-319-18841-6.
Resources
Moodle.
Teaching method
null
Evaluation method
There will be a final exam worth 40% of your final grade. The remaining part of your grade will be allocated to two Projects (team of 3 or 4 students) (20%), a midterm exam (30%) and evaluation of practical classes (10%).
Individual Assignments: there will be one midterm exam on April 11 and a final exam on June 14, 09:30. A minimum grade of 45% in the individual component is required to pass the course.
Team Assignments: to carry out the group assignments, each student must work in a team of 3/4 students. The first assignment is due on March 30 at 10PM and the second assignment is due on the 15th of May at 10PM (all files must be uploaded in moodle). Late assignments are not accepted. Practical classes: the evaluation of practical classes will be based on participation in class (including attendance) and problems sets that students are supposed to deliver.Regular exam period:
See assessment above.
Resit exam period:
There will be only a final exam, on June 30, 11:00, worth 100% of your final grade.
Grade improvement in regular period:
See assessment above.
Grade improvement in resit period:
See assessment above.